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bioRxiv · 10.1101/2022.04.10.487815

Single-cell colocalization analysis using a deep generative model

Kojima, Y.·Mii, S.·Hayashi, S.·Hirose, H.·Ishikawa, M.·Akiyama, M.·Enomoto, A.·Shimamura, T.
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Abstract

1Analyzing colocalization of single cells with heterogeneous molecular phenotypes is essential for understanding cell-cell interactions, cellular responses to external stimuli, and their biological functions in diseases and tissues. However, high-throughput methods for identifying spatial proximity at single-cell resolution are practically unavailable. Here, we introduce DeepCOLOR, a computational framework based on a deep generative model that recovers inter-cellular colocalization networks with single cell resolution by the integration of single cell and spatial transcriptomes. It segregates cell populations defined by the colocalization relationships and predicts cell-cell interactions between colocalized single cells. DeepCOLOR could identify plausible cell-cell interaction candidates in mouse brain tissues, human squamous cell carcinoma samples, and human lung tissues infected with SARS-CoV-2 by reconstructing spatial colocalization maps at single-cell resolution. DeepCOLOR is typically applicable to studying cell-cell interactions in any spatial niche. Our newly developed computational framework could help uncover molecular pathways across single cells connected with colocalization networks.

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Kojima, Y., Mii, S., Hayashi, S., Hirose, H., Ishikawa, M., Akiyama, M., Enomoto, A., Shimamura, T.. 2022-04-11. Single-cell colocalization analysis using a deep generative model. https://doi.org/10.1101/2022.04.10.487815

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